Quantum Genetic Algorithm (QGA) is a promising area in the field of computational intelligence nowadays. Although some genetic algorithms to find minimal reduct of attributes have been proposed, most of them have some defects. On the other hand, quantum genetic algorithm has some advantages, such as strong parallelism, rapid good search capability, and small population size. In this paper, we propose a QGA to find minimal reduct based on distinction table. The algorithm can obtain the best solution with one chromosome in a short time. It is testified by two experiments that our algorithm improves the GA from four points of view: population size, parallelism, computing time and search capability.
{"title":"Application of Quantum Genetic Algorithm on Finding Minimal Reduct","authors":"M. Qadir, M. Fahad, Syed Adnan Hussain Shah","doi":"10.1109/GrC.2007.87","DOIUrl":"https://doi.org/10.1109/GrC.2007.87","url":null,"abstract":"Quantum Genetic Algorithm (QGA) is a promising area in the field of computational intelligence nowadays. Although some genetic algorithms to find minimal reduct of attributes have been proposed, most of them have some defects. On the other hand, quantum genetic algorithm has some advantages, such as strong parallelism, rapid good search capability, and small population size. In this paper, we propose a QGA to find minimal reduct based on distinction table. The algorithm can obtain the best solution with one chromosome in a short time. It is testified by two experiments that our algorithm improves the GA from four points of view: population size, parallelism, computing time and search capability.","PeriodicalId":259430,"journal":{"name":"2007 IEEE International Conference on Granular Computing (GRC 2007)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127253823","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Matching of heterogeneous knowledge sources is of increasing importance in areas such as scientific knowledge management, e-commerce, enterprise application integration, and many emerging Semantic Web applications. With the desire of knowledge sharing and reuse in these fields, it is common that the knowledge coming from different organizations from the same domain is to be matched. We propose a knowledge matching method based on our previously developed tree mining algorithms for extracting frequently occurring subtrees from a tree structured database such as XML. Using the method the common structure among the different representations can be automatically extracted. Our focus is on knowledge matching at the structural level and we use a set of example XML schema documents from the same domain to evaluate the method. We discuss some important issues that arise when applying tree mining algorithms for detection of common document structures. The experiments demonstrate the usefulness of the approach.
{"title":"Tree Mining Application to Matching of Heterogeneous Knowledge Representations","authors":"F. Hadzic, T. Dillon, E. Chang","doi":"10.1109/GrC.2007.134","DOIUrl":"https://doi.org/10.1109/GrC.2007.134","url":null,"abstract":"Matching of heterogeneous knowledge sources is of increasing importance in areas such as scientific knowledge management, e-commerce, enterprise application integration, and many emerging Semantic Web applications. With the desire of knowledge sharing and reuse in these fields, it is common that the knowledge coming from different organizations from the same domain is to be matched. We propose a knowledge matching method based on our previously developed tree mining algorithms for extracting frequently occurring subtrees from a tree structured database such as XML. Using the method the common structure among the different representations can be automatically extracted. Our focus is on knowledge matching at the structural level and we use a set of example XML schema documents from the same domain to evaluate the method. We discuss some important issues that arise when applying tree mining algorithms for detection of common document structures. The experiments demonstrate the usefulness of the approach.","PeriodicalId":259430,"journal":{"name":"2007 IEEE International Conference on Granular Computing (GRC 2007)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125506529","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Most of current spam email detection systems use keywords in a blacklist to detect spam emails. However these keywords can be written as misspellings, for example "baank", "ba-nk" and "bankk" instead of "bank". Moreover, misspellings are changed from time to time and hence spam email detection system needs to constantly update the blacklist to detect spam emails containing such misspellings. However it is impossible to predict all possible misspellings for a given keyword to add those to the blacklist. We present a possibility theory-based approach to spam email detection to solve this problem. We consider every keyword in the blacklist along with its misspellings as a fuzzy set and propose a possibility function. This function will be used to calculate a possibility score for an unknown email. Using a proposed if-then rule and this core, we can decide whether or not this unknown email is spam. Experimental results are also presented.
{"title":"Possibility Theory-Based Approach to Spam Email Detection","authors":"D. Tran, Wanli Ma, D. Sharma, Thien Huu Nguyen","doi":"10.1109/GrC.2007.123","DOIUrl":"https://doi.org/10.1109/GrC.2007.123","url":null,"abstract":"Most of current spam email detection systems use keywords in a blacklist to detect spam emails. However these keywords can be written as misspellings, for example \"baank\", \"ba-nk\" and \"bankk\" instead of \"bank\". Moreover, misspellings are changed from time to time and hence spam email detection system needs to constantly update the blacklist to detect spam emails containing such misspellings. However it is impossible to predict all possible misspellings for a given keyword to add those to the blacklist. We present a possibility theory-based approach to spam email detection to solve this problem. We consider every keyword in the blacklist along with its misspellings as a fuzzy set and propose a possibility function. This function will be used to calculate a possibility score for an unknown email. Using a proposed if-then rule and this core, we can decide whether or not this unknown email is spam. Experimental results are also presented.","PeriodicalId":259430,"journal":{"name":"2007 IEEE International Conference on Granular Computing (GRC 2007)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130332997","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper considers anomaly network traffic detection using different network feature subsets. Fuzzy c-means vector quantization is used to train network attack models and the minimum distortion rule is applied to detect network attacks. We also demonstrate the effectiveness and ineffectiveness in finding anomalies by looking at the network data alone. Experiments performed on the KDD CUP 1999 dataset show that time based traffic features in the last two second time window should be selected to obtain highest detection rates.
本文考虑使用不同的网络特征子集来检测异常网络流量。利用模糊c均值矢量量化训练网络攻击模型，利用最小失真规则检测网络攻击。我们还演示了通过单独查看网络数据来发现异常的有效性和无效性。在KDD CUP 1999数据集上进行的实验表明，为了获得最高的检测率，应该选择最后两秒时间窗的基于时间的交通特征。
{"title":"Fuzzy Vector Quantization for Network Intrusion Detection","authors":"D. Tran, Wanli Ma, D. Sharma, Thien Huu Nguyen","doi":"10.1109/GrC.2007.124","DOIUrl":"https://doi.org/10.1109/GrC.2007.124","url":null,"abstract":"This paper considers anomaly network traffic detection using different network feature subsets. Fuzzy c-means vector quantization is used to train network attack models and the minimum distortion rule is applied to detect network attacks. We also demonstrate the effectiveness and ineffectiveness in finding anomalies by looking at the network data alone. Experiments performed on the KDD CUP 1999 dataset show that time based traffic features in the last two second time window should be selected to obtain highest detection rates.","PeriodicalId":259430,"journal":{"name":"2007 IEEE International Conference on Granular Computing (GRC 2007)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129755423","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Granular computing unifies structured thinking, structured problem solving and structured information processing. In order to see the flexibility and universal applicability of this trinity model, we must demonstrate its effectiveness in solving real world problems. In this paper, we apply the basic ideas, principles, and strategies of granular computing to the specific problem solving task known as structured writing. Results from languages, human knowledge organization, rhetoric, writing, computer programming, and mathematical proving are summarized and cast in a setting for structured writing. The results bring new insights into granular computing.
{"title":"Structured Writing with Granular Computing Strategies","authors":"Yiyu Yao","doi":"10.1109/GrC.2007.15","DOIUrl":"https://doi.org/10.1109/GrC.2007.15","url":null,"abstract":"Granular computing unifies structured thinking, structured problem solving and structured information processing. In order to see the flexibility and universal applicability of this trinity model, we must demonstrate its effectiveness in solving real world problems. In this paper, we apply the basic ideas, principles, and strategies of granular computing to the specific problem solving task known as structured writing. Results from languages, human knowledge organization, rhetoric, writing, computer programming, and mathematical proving are summarized and cast in a setting for structured writing. The results bring new insights into granular computing.","PeriodicalId":259430,"journal":{"name":"2007 IEEE International Conference on Granular Computing (GRC 2007)","volume":"108 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134346580","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Chen Wu, Xiao-lin Hu, Xiajiong Shen, Xiaodan Zhang, Yi Pan
The present paper puts forward an incremental algorithm for extracting default definite rules proposed by us from incomplete decision table using semi-equivalence classes derived from a semi-equivalence relation and their meet and join blocks on the universe. After default definite decision rules and constraint rules are acquired from the incomplete decision table, the incremental algorithm is used to modify them when new data is added to the incomplete information table. It does not need to process the original dataset repeatedly but only updates related data and rules. So it is effective in performing mining tasks from incomplete decision table. Through an example, a procedure for mining and revising rules is illustrated.
{"title":"An Incremental Algorithm for Mining Default Definite Decision Rules from Incomplete Decision Tables","authors":"Chen Wu, Xiao-lin Hu, Xiajiong Shen, Xiaodan Zhang, Yi Pan","doi":"10.1109/GrC.2007.57","DOIUrl":"https://doi.org/10.1109/GrC.2007.57","url":null,"abstract":"The present paper puts forward an incremental algorithm for extracting default definite rules proposed by us from incomplete decision table using semi-equivalence classes derived from a semi-equivalence relation and their meet and join blocks on the universe. After default definite decision rules and constraint rules are acquired from the incomplete decision table, the incremental algorithm is used to modify them when new data is added to the incomplete information table. It does not need to process the original dataset repeatedly but only updates related data and rules. So it is effective in performing mining tasks from incomplete decision table. Through an example, a procedure for mining and revising rules is illustrated.","PeriodicalId":259430,"journal":{"name":"2007 IEEE International Conference on Granular Computing (GRC 2007)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133704408","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
S. Yamaguchi, K. Nagamune, K. Oe, Syoji Kobashi, K. Kondo, Y. Hata
This paper introduces an ultrasound identification system for cellular quantity of artificial culture bone with fuzzy inference. In our method, first, we measure ultrasound wave. Second, we obtain the two characteristics of the amplitude and the frequency. The amplitude is calculated as the Peak to Peak value, and the frequency is calculated from frequency spectrum of transfer-function by using cross-spectrum method. Our fuzzy inferences system estimates the cellular quantity from these values. As an experimental result, our identification system could evaluate the cellular quantity in culture bone with high accuracy.
{"title":"Fuzzy Logic Approach to Identification of Cellular Quantity by Ultrasonic System","authors":"S. Yamaguchi, K. Nagamune, K. Oe, Syoji Kobashi, K. Kondo, Y. Hata","doi":"10.1109/GrC.2007.68","DOIUrl":"https://doi.org/10.1109/GrC.2007.68","url":null,"abstract":"This paper introduces an ultrasound identification system for cellular quantity of artificial culture bone with fuzzy inference. In our method, first, we measure ultrasound wave. Second, we obtain the two characteristics of the amplitude and the frequency. The amplitude is calculated as the Peak to Peak value, and the frequency is calculated from frequency spectrum of transfer-function by using cross-spectrum method. Our fuzzy inferences system estimates the cellular quantity from these values. As an experimental result, our identification system could evaluate the cellular quantity in culture bone with high accuracy.","PeriodicalId":259430,"journal":{"name":"2007 IEEE International Conference on Granular Computing (GRC 2007)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133638905","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Summary form only given. First, we briefly advocate a need of natural language based methods in data mining, notably whew domain experts have a limited knowledge of modern tools of information technology. We present some approaches to linguistic summarization of sets of (numeric and/or textual) data, and show that a fuzzy logic based approach by Yager (1982), notably in its extended and implementable version of Kacprzyk and Yager (2001), and Kacprzyk, Yager and Zadrozny (2000), offers a simplicity and intuitive appeal, in particular in its new setting by Kacprzyk and Zadrozny (2005) based on Zadeh's computing with words and protoforms.
{"title":"Linguistic Summaries of Static and Dynamic Data: Computing with Words and Granularity","authors":"J. Kacprzyk","doi":"10.1109/GrC.2007.161","DOIUrl":"https://doi.org/10.1109/GrC.2007.161","url":null,"abstract":"Summary form only given. First, we briefly advocate a need of natural language based methods in data mining, notably whew domain experts have a limited knowledge of modern tools of information technology. We present some approaches to linguistic summarization of sets of (numeric and/or textual) data, and show that a fuzzy logic based approach by Yager (1982), notably in its extended and implementable version of Kacprzyk and Yager (2001), and Kacprzyk, Yager and Zadrozny (2000), offers a simplicity and intuitive appeal, in particular in its new setting by Kacprzyk and Zadrozny (2005) based on Zadeh's computing with words and protoforms.","PeriodicalId":259430,"journal":{"name":"2007 IEEE International Conference on Granular Computing (GRC 2007)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116747651","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yu-Chin Cheng, Chien-Hung Chen, Chung-Chih Chiang, Jun-Wei Wang, C. Laih
With the incoming of information era, Internet has been developed rapidly and offered more and more services. However, intrusions, viruses and worms follow with the grown of Internet, spread widely all over the world within high speed network. Although many kinds of intrusion detection systems (IDSs) are developed, they have some disadvantages in that they focus on low-level attacks or anomalies, and raise alerts independently. In this paper, we give a formal description about attack patterns, attack transition states and attack scenarios. We proposed the system architecture to generate an attack scenario database correctly and completely. We first classify and extract attack patterns, then, correlate attack patterns with pre/post conditions matching and. Moreover, the approach, attack scenario generation with casual relationship (ASGCR), is proposed to build an attack scenario database Finally, we present the combination of our attack scenario database with security operation center (SOC) to implement the related components concerning alert integrations and correlations. It is shown that our method is better than CAML [4] since we can generate more attack scenarios effectively and correctly to help system managers to maintain network security.
{"title":"Generating Attack Scenarios with Causal Relationship","authors":"Yu-Chin Cheng, Chien-Hung Chen, Chung-Chih Chiang, Jun-Wei Wang, C. Laih","doi":"10.1109/GrC.2007.117","DOIUrl":"https://doi.org/10.1109/GrC.2007.117","url":null,"abstract":"With the incoming of information era, Internet has been developed rapidly and offered more and more services. However, intrusions, viruses and worms follow with the grown of Internet, spread widely all over the world within high speed network. Although many kinds of intrusion detection systems (IDSs) are developed, they have some disadvantages in that they focus on low-level attacks or anomalies, and raise alerts independently. In this paper, we give a formal description about attack patterns, attack transition states and attack scenarios. We proposed the system architecture to generate an attack scenario database correctly and completely. We first classify and extract attack patterns, then, correlate attack patterns with pre/post conditions matching and. Moreover, the approach, attack scenario generation with casual relationship (ASGCR), is proposed to build an attack scenario database Finally, we present the combination of our attack scenario database with security operation center (SOC) to implement the related components concerning alert integrations and correlations. It is shown that our method is better than CAML [4] since we can generate more attack scenarios effectively and correctly to help system managers to maintain network security.","PeriodicalId":259430,"journal":{"name":"2007 IEEE International Conference on Granular Computing (GRC 2007)","volume":"187 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124740800","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Association rule mining is one of the most important issues in data mining. Apriori computation schemes greatly reduce the computation time by pruning the candidate item-set. However, a large computation time is required when the treated data are dense and the amount of data is large. With apriori methods, the problem of becoming incomputable cannot be avoided when the total number of items is large. On the other hand, bottom-up approaches such as artificial life approaches are the opposite to of the top-down approaches of searches covering all transactions, and may provide new methods of breaking away from the completeness of searches in conventional algorithms. Here, an artificial life data mining technique is proposed in which one transaction is considered as one individual, and association rules are accumulated by the interaction of randomly selected individuals. The proposed algorithm is compared to other methods in application to a large-scale actual dataset, and it is verified that its performance is greatly superior to that of the method using transaction data virtually divided and that of apriori method by sampling approach, thus demonstrating its usefulness.
{"title":"Speed-up Technique for Association Rule Mining Based on an Artificial Life Algorithm","authors":"Masaaki Kanakubo, M. Hagiwara","doi":"10.1109/GrC.2007.103","DOIUrl":"https://doi.org/10.1109/GrC.2007.103","url":null,"abstract":"Association rule mining is one of the most important issues in data mining. Apriori computation schemes greatly reduce the computation time by pruning the candidate item-set. However, a large computation time is required when the treated data are dense and the amount of data is large. With apriori methods, the problem of becoming incomputable cannot be avoided when the total number of items is large. On the other hand, bottom-up approaches such as artificial life approaches are the opposite to of the top-down approaches of searches covering all transactions, and may provide new methods of breaking away from the completeness of searches in conventional algorithms. Here, an artificial life data mining technique is proposed in which one transaction is considered as one individual, and association rules are accumulated by the interaction of randomly selected individuals. The proposed algorithm is compared to other methods in application to a large-scale actual dataset, and it is verified that its performance is greatly superior to that of the method using transaction data virtually divided and that of apriori method by sampling approach, thus demonstrating its usefulness.","PeriodicalId":259430,"journal":{"name":"2007 IEEE International Conference on Granular Computing (GRC 2007)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130313917","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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